Cross-domain video concept detection using adaptive svms

Proceedings of the ACM International Multimedia Conference and Exhibition

Volumn , Issue , 2007, Pages 188-197

  Yang, Jun ^a   Yan, Rong ^b   Hauptmann, Alexander G ^a  

^a Carnegie Mellon University   (United States)

^b IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

Adaptive SVMs; Classifier adaptation; Cross domain video concept detection

Indexed keywords

ADAPTIVE SVM; CROSS-DOMAIN VIDEO CONCEPT DETECTION; META FEATURES;

DATA STRUCTURES; PROBLEM SOLVING; SUPPORT VECTOR MACHINES;

MULTIMEDIA SERVICES;

EID: 37849026107     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1291233.1291276     Document Type: Conference Paper

References (19)

1. G. Cauwenberghs and T. Poggio. Incremental and decrementai support vector machine learning. In Proc. of Neural Information Processing Systems.
2. S. J. Delany, P. Cunningham, A. Tsymbal, and L. Coyle. A case-based technique for tracking concept drift in spam filtering. Knowledge-Based Systems, 18(4-5):187-195, 2005.
3. D. Ding and B. Zhang. Probabilistic model supported rank aggregation for the semantic concept detection in video. In Proc. of ACM Int'l Conf. on Image and Video Retrieval, 2007.
4. H. Drucker, C. J. Burges, L. Kaufman, A. Smola, and V. Vapnik. Support vector regression machines, pages 155-161, 1996.
5. T. Hastie, R. Tibshirani, and J. Friedman. The elements of statistical learning: Data mining, inference, and prediction.
6. R. Klinkenberg and T. Joachims. Detecting concept drift with support vector machines. In Proc. of Int'l Conf. on Machine Learning, pages 487-494, 2000.
7. J. Z. Kolter and M. A. Maloof. Dynamic weighted majority: A new ensemble method for tracking concept drift. In Proc. of 3rd IEEE Int'l Conf. on Data Mining, page 123, 2003.
8. X. Liao, Y. Xue, and L. Carin. Logistic regression with an auxiliary data source. In Proc. of the 22nd Int'l Conf. on Machine learning, pages 505-512, New York, NY, USA, 2005. ACM Press.
9. R. Manmatha, T. Rath, and F. Feng. Modeling score distributions for combining the outputs of search engines. In Proc. of the 24th Annual Int'l ACM SIGIR conf. on Research and Development in Information Retrieval, pages 267-275, New York, NY, USA, 2001.
10. M. R. Naphade, L. Kennedy, J. R. Kender, S. F. Chang, J. Smith, P. Over, and A. Hauptmann. A light scale concept ontology for multimedia understanding for TRECVID 2005. In IBM Research Technical Report, 2005.
11. A. P. Natsev, M. R. Naphade, and J. Tesic. Learning the semantics of multimedia queries and concepts from a small number of examples. In Proc. of 13th Annual ACM Int'l Conf. on Multimedia, pages 598-607, 2005.
12. J. Piatt. Fast training of support vector machines using sequential minimal optimization. Advances in kernel methods: support vector learning, pages 185-208, 1999.
13. A. Smeaton and P. Over. Trecvid: Benchmarking the effectiveness of infomration retrieval tasks on digital video. In Proc. of the Intl. Conf. on Image and Video Retrieval, 2003.
14. C. G. M. Snoek, M. Worring, J. C. van Gemert, J.-M. Geusebroek, and A. W. M. Smeulders. The challenge problem for automated detection of 101 semantic concepts in multimedia. In Prof, of 14th Annual ACM Int'l Conf. Multimedia, pages 421-430, 2006.
15. N. Syed, H. Liu, and K. Sung. Incremental learning with support vector machines. In In Proc. of the Workshop on Support Vector Machines, at the Int'l Joint Conf. on Articial Intelligence, 1999.
16. H. Wang, W. Fan, P. S. Yu, and J. Han. Mining concept-drifting data streams using ensemble classifiers. In Proc. of 9th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining, pages 226-235, 2003.
17. P. Wu and T. G. Dietterich. Improving SVM accuracy by training on auxiliary data sources. In Proc. of Int'l Conf. on Machine learning, page 110, 2004.
18. X. Wu and R. Srihari. Incorporating prior knowledge with weighted margin support vector machines. In Proc. of the 10th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining, pages 326-333, 2004.
19. Y. Wu, E. Y. Chang, K. C.-C. Chang, and J. R. Smith. Optimal multimodal fusion for multimedia data analysis. In Proc. of the 12th annual ACM Int'l Conf. on Multimedia, pages 572-579, 2004.